Preparing for Successful Enterprise AI with Kensho Technologies

Enterprise AI has moved beyond the pilot stage. Boards want returns. Businesses want production systems. And somewhere in the middle, technologists are working to build the infrastructure that makes all of it possible without sacrificing the rigor that regulated industries demand. Solidigm’s Jeniece Wnorowski and I recently spoke to Adity Dokania, director of cloud infrastructure and security at Kensho Technologies, an AI and data analytics company owned by financial giant S&P Global. From her seat in the industry, she’s dealing with the opportunities and the complexities of AI adoption daily as she brings frontier capabilities to work for Kensho’s enterprise clients.

From Exploration to Accountability

The place where Adity has observed the greatest change among enterprise clients using AI in the past year is not in their technologies, but their attitudes. Twelve months ago, organizations were largely in exploration mode, running pilots and even learning the vocabulary of AI. Today, the questions have changed. “The conversation has moved from ‘is it even possible’ to being accountable,” she said. “Boards are asking for ROIs. Businesses are asking for something within production.”

That shift is healthy, she notes, but it has created real pressure. The governance frameworks and infrastructure required to support production-grade AI are still being built in most organizations. The result is a tension between leadership pushing for speed and technical and risk teams laying the groundwork to do things correctly for successful ecosystem deployments.

The Friction Points Holding Enterprises Back

When asked where AI adoption runs into the most friction, Adity pointed to three interconnected challenges: data quality, infrastructure readiness, and organizational structure.

In terms of data quality, she clarified that the main issue wasn’t one of data cleanliness, but of trust. “I mean data that’s trusted, that has lineage, that people inside the organization actually agree upon,” she said. For infrastructure readiness, legacy systems were not built for AI workloads, and retrofitting them creates complexity that slows progress. But beyond technology, Adity argued that organizational boundaries may be the most underappreciated obstacle. “AI doesn’t fit neatly into existing team boundaries. It requires collaboration between legal, compliance, business, and engineering, all of them coming together simultaneously.”

That structural challenge, she believes, is what most often determines how quickly something can actually reach the market.

A Framework for Readiness

Adity has developed a practical framework for evaluating when an AI capability is ready to move from experimentation into production. She applies three tests. The first is observability: can the system be monitored after deployment? The second is explainability: even if the system is non-deterministic, can you trace why it reached a particular decision? The third is resilience: “What happens when the system is wrong?” she asked. “Because it will be wrong sometimes. The question is whether it fails in a way that’s recoverable and detectable.”

This framework that Adity uses to evaluate technologies actually predates AI. In an earlier iteration, explainability was instead reproducibility, a quality which is not achievable with non-deterministic generative AI models that by design will not give the same answer twice. Thanks to this history, the framework reflects maturity in thinking about AI risk. Rather than asking whether a system can be made perfect, Adity asks whether it can be observed, understood, and its failures managed.

Watching for Real Signals

Looking toward the rest of 2026, Adity is focused on two indicators that would signal AI maturity across the enterprise market. The first is whether AI begins appearing in operating metrics rather than project reports. When a chief financial officer references AI-driven efficiency on an earnings call as a current contributor rather than a future initiative, something has genuinely changed.

The second signal is hiring patterns, particularly “when enterprises start hiring for AI operations and AI governance roles, not just data scientists and engineers,” she said. The latter reflects a shift in focus around governance and around operational AI. Both indicators point to the same underlying shift: AI moving from a discrete initiative into the fabric of how businesses actually run.

Building the Foundation That Makes AI Sustainable

For Kensho Technologies’ partners and clients, Adity points to three priorities that matter most right now. First is governance infrastructure, the model gateways, observability layers, and audit trails she describes candidly as “the boring stuff that makes everything else sustainable.” Second is use case prioritization, working with leadership to identify where AI can have disproportionate impact rather than pursuing every opportunity at once. Third is integration over replacement, meeting clients where they are and building AI into existing workflows rather than handing them a new interface and expecting them to adapt.

Taken together, these priorities reflect a consistent philosophy: the organizations that will benefit most from AI are not necessarily the ones that move the fastest, but the ones that build the infrastructure to sustain what they build.

The TechArena Take

Adity’s argument that the organizations making real progress in AI deployment are those investing in the scaffolding that makes AI deployable, defensible, and durable uncovers a truth that more and more organizations are coming to embrace. Governance, observability, and cross-functional ownership are the preconditions for AI deployments that actually stick. While an ambitious vision may garner media attention, it’s the “boring” homework that ensures that investments in AI actually pay off for enterprise users.

To learn more, listen to the full podcast or visit kensho.com.